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• W3207877391 abstract "Opposition-based learning (OBL), which plays an important role in soft computing, has recently drawn attention. The paramount challenge of OBL is to design and find an OBL strategy that is suitable for the problem structure. Besides, for the OBL variants proposed so far, there is no clear taxonomy guideline. To solve these issues, this paper proposes a novel opposition, called dual opposition-based learning (DOBL), which contains two opposition strategies and a protective mechanism. Firstly, a diversity-based taxonomy is proposed, which categorizes existing state-of-the-art OBL variants according to dimension-wise diversity. The subpopulation strategy is used and embedded in the classified OBL variants to generate explorative opposition and exploitative opposition. Secondly, for a successful algorithm, a good ratio between exploration and exploitation is required. Therefore, a protective mechanism is designed to obtain a good equilibrium between exploration and exploitation. Finally, the performance of DOBL is compared with eight state-of-the-art OBL variants on DE and advanced DE named jSO to find the CEC 2017 test suite’s best solution. Besides, DOBL is applied to CEC 2011 as well as CEC 2020 real-world optimization problems, and compared with nine novel metaheuristic algorithms as well as the top three algorithms in CEC 2020, respectively. Two statistical tests, the Wilcoxon rank-sum test and the Friedman test, are used to analyze the experiment results. The experiment results of 29 functions and 60 real-world problems demonstrate that the proposed DOBL is better than its competitors on CEC2011, CEC 2017, and CEC 2020 test suites." @default.
• W3207877391 created "2021-10-25" @default.
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• W3207877391 date "2021-12-01" @default.
• W3207877391 modified "2023-10-18" @default.
• W3207877391 title "A dual opposition-based learning for differential evolution with protective mechanism for engineering optimization problems" @default.
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• W3207877391 doi "https://doi.org/10.1016/j.asoc.2021.107942" @default.
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